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Multidimensional social exposure clusters and incident multimorbidity in a population-based cohort

Journal
American journal of epidemiology (Q1)
Published
18 July 2026
Study design
Unclassified
Evidence level
Level 5, Expert Opinion (CEBM 5)
Authors
Ingrid Giesinger, Emmalin Buajitti, Arjumand Siddiqi, Peter M Smith, Rahul Krishnan, Gemma Postill, et al.
PMID
42470091
DOI
10.1093/aje/kwag172

Why clinicians should know about it

Abstract

Multimorbidity follows a strong social gradient, yet how the co-occurrence of social determinants shapes risk remains poorly understood. Most research examines individual determinants in isolation, potentially obscuring socially patterned subgroups relevant to multimorbidity risk. This study examines the association between multidimensional social exposure profiles (derived from individual- and area-level determinants) and multimorbidity risk. Profiles were derived through unsupervised clustering applied to population-based cohort of Ontario respondents to the Canadian Community Health Survey (2001-2011) and linked area-level Census measures. Chronic disease, and subsequent multimorbidity status were identified through linkage to administrative health data until 2022. Sex-stratified Weibull models were used to estimate hazard ratios for the association between multidimensional social exposure and incident multimorbidity. Unsupervised clustering identified six distinct social exposure profiles, with multimorbidity onset differing across profiles and elevated risk observed across multiple patterns of social disadvantage, rather than following a simple graded gradient. In sex-stratified analyses, disadvantage social exposure profiles were associated with an earlier multimorbidity onset among females (adjusted HR: 1.70, 95%CI: 1.18-2.53) and males (adjusted HR: 1.59, 95%CI: 1.20,2.05). These multidimensional social exposure profiles capture population-level social patterning associated with differential multimorbidity risk. These findings have implications for upstream policy aimed at reducing inequities in multimorbidity.

Abstract as published, via PubMed.

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For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.